Overview

Dataset statistics

Number of variables15
Number of observations341
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.1 KiB
Average record size in memory120.4 B

Variable types

Unsupported1
Categorical2
Numeric12

Alerts

Symbol has constant value "RELIANCE" Constant
Series has constant value "EQ" Constant
Prev Close is highly correlated with Open and 5 other fieldsHigh correlation
Open is highly correlated with Prev Close and 5 other fieldsHigh correlation
High is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close is highly correlated with Prev Close and 5 other fieldsHigh correlation
VWAP is highly correlated with Prev Close and 5 other fieldsHigh correlation
Volume is highly correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly correlated with Volume and 2 other fieldsHigh correlation
Trades is highly correlated with Volume and 2 other fieldsHigh correlation
Deliverable Volume is highly correlated with Volume and 2 other fieldsHigh correlation
Series is highly correlated with SymbolHigh correlation
Symbol is highly correlated with SeriesHigh correlation
VWAP has unique values Unique
Volume has unique values Unique
Turnover has unique values Unique
Trades has unique values Unique
Deliverable Volume has unique values Unique
Date is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-11 04:24:12.961542
Analysis finished2022-09-11 04:25:03.258495
Duration50.3 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Date
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size2.8 KiB

Symbol
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
RELIANCE
341 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters2728
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRELIANCE
2nd rowRELIANCE
3rd rowRELIANCE
4th rowRELIANCE
5th rowRELIANCE

Common Values

ValueCountFrequency (%)
RELIANCE341
100.0%

Length

2022-09-11T09:55:03.426207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T09:55:03.691772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
reliance341
100.0%

Most occurring characters

ValueCountFrequency (%)
E682
25.0%
R341
12.5%
L341
12.5%
I341
12.5%
A341
12.5%
N341
12.5%
C341
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2728
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E682
25.0%
R341
12.5%
L341
12.5%
I341
12.5%
A341
12.5%
N341
12.5%
C341
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E682
25.0%
R341
12.5%
L341
12.5%
I341
12.5%
A341
12.5%
N341
12.5%
C341
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E682
25.0%
R341
12.5%
L341
12.5%
I341
12.5%
A341
12.5%
N341
12.5%
C341
12.5%

Series
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
EQ
341 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters682
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ341
100.0%

Length

2022-09-11T09:55:03.846956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T09:55:04.018792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
eq341
100.0%

Most occurring characters

ValueCountFrequency (%)
E341
50.0%
Q341
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter682
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E341
50.0%
Q341
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin682
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E341
50.0%
Q341
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E341
50.0%
Q341
50.0%

Prev Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct335
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2404.766862
Minimum1913.15
Maximum2819.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:04.237489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1913.15
5-th percentile1997.05
Q12244.9
median2423.9
Q32572.85
95-th percentile2700.4
Maximum2819.85
Range906.7
Interquartile range (IQR)327.95

Descriptive statistics

Standard deviation213.8930387
Coefficient of variation (CV)0.08894543669
Kurtosis-0.5672917834
Mean2404.766862
Median Absolute Deviation (MAD)156.25
Skewness-0.4665628634
Sum820025.5
Variance45750.232
MonotonicityNot monotonic
2022-09-11T09:55:04.721750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2627.42
 
0.6%
2425.62
 
0.6%
20862
 
0.6%
2227.42
 
0.6%
2378.32
 
0.6%
2700.42
 
0.6%
1997.31
 
0.3%
2626.051
 
0.3%
2663.71
 
0.3%
2655.851
 
0.3%
Other values (325)325
95.3%
ValueCountFrequency (%)
1913.151
0.3%
1916.61
0.3%
1920.11
0.3%
1926.21
0.3%
19311
0.3%
1931.751
0.3%
1933.151
0.3%
1937.31
0.3%
1959.051
0.3%
1960.351
0.3%
ValueCountFrequency (%)
2819.851
0.3%
2798.751
0.3%
2790.251
0.3%
2782.11
0.3%
2780.451
0.3%
2779.51
0.3%
2778.351
0.3%
2775.651
0.3%
2772.751
0.3%
2767.551
0.3%

Open
Real number (ℝ≥0)

HIGH CORRELATION

Distinct315
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2406.898094
Minimum1915
Maximum2856.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:05.002934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1915
5-th percentile1997.9
Q12250
median2430.95
Q32574.9
95-th percentile2710
Maximum2856.15
Range941.15
Interquartile range (IQR)324.9

Descriptive statistics

Standard deviation212.9058893
Coefficient of variation (CV)0.08845654489
Kurtosis-0.5570421691
Mean2406.898094
Median Absolute Deviation (MAD)151.7
Skewness-0.4628813499
Sum820752.25
Variance45328.91769
MonotonicityNot monotonic
2022-09-11T09:55:05.284119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24003
 
0.9%
24523
 
0.9%
26003
 
0.9%
25603
 
0.9%
26103
 
0.9%
24302
 
0.6%
21742
 
0.6%
23802
 
0.6%
23732
 
0.6%
20582
 
0.6%
Other values (305)316
92.7%
ValueCountFrequency (%)
19151
0.3%
1921.851
0.3%
1923.351
0.3%
1928.051
0.3%
1930.11
0.3%
1937.31
0.3%
1937.751
0.3%
19391
0.3%
19501
0.3%
19661
0.3%
ValueCountFrequency (%)
2856.151
0.3%
2809.951
0.3%
27851
0.3%
27801
0.3%
2772.751
0.3%
2771.91
0.3%
2769.91
0.3%
27621
0.3%
2758.91
0.3%
2755.851
0.3%

High
Real number (ℝ≥0)

HIGH CORRELATION

Distinct328
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2433.616569
Minimum1932.9
Maximum2856.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:05.534058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1932.9
5-th percentile2016.45
Q12275.85
median2457
Q32598.75
95-th percentile2742.75
Maximum2856.15
Range923.25
Interquartile range (IQR)322.9

Descriptive statistics

Standard deviation216.9498059
Coefficient of variation (CV)0.08914707793
Kurtosis-0.5298882719
Mean2433.616569
Median Absolute Deviation (MAD)149
Skewness-0.4741761527
Sum829863.25
Variance47067.21829
MonotonicityNot monotonic
2022-09-11T09:55:05.799621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24252
 
0.6%
22352
 
0.6%
25002
 
0.6%
26102
 
0.6%
26382
 
0.6%
25702
 
0.6%
26062
 
0.6%
24052
 
0.6%
24422
 
0.6%
2598.752
 
0.6%
Other values (318)321
94.1%
ValueCountFrequency (%)
1932.91
0.3%
19351
0.3%
1938.51
0.3%
1938.551
0.3%
19421
0.3%
1946.81
0.3%
1955.651
0.3%
1963.451
0.3%
1967.81
0.3%
19791
0.3%
ValueCountFrequency (%)
2856.151
0.3%
28511
0.3%
28281
0.3%
2817.351
0.3%
28141
0.3%
2805.51
0.3%
28031
0.3%
28021
0.3%
27951
0.3%
2791.11
0.3%

Low
Real number (ℝ≥0)

HIGH CORRELATION

Distinct323
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2380.575513
Minimum1906
Maximum2786.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:06.065185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1906
5-th percentile1986
Q12234.15
median2404
Q32547.35
95-th percentile2669.3
Maximum2786.1
Range880.1
Interquartile range (IQR)313.2

Descriptive statistics

Standard deviation208.9268729
Coefficient of variation (CV)0.08776317816
Kurtosis-0.5785960503
Mean2380.575513
Median Absolute Deviation (MAD)152.1
Skewness-0.4533509196
Sum811776.25
Variance43650.43821
MonotonicityNot monotonic
2022-09-11T09:55:06.330763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23603
 
0.9%
25703
 
0.9%
24953
 
0.9%
23703
 
0.9%
24043
 
0.9%
24122
 
0.6%
24612
 
0.6%
20812
 
0.6%
25012
 
0.6%
23052
 
0.6%
Other values (313)316
92.7%
ValueCountFrequency (%)
19061
0.3%
1906.61
0.3%
19071
0.3%
1908.051
0.3%
19101
0.3%
19111
0.3%
1920.951
0.3%
19261
0.3%
1930.41
0.3%
1943.11
0.3%
ValueCountFrequency (%)
2786.11
0.3%
2777.31
0.3%
2758.051
0.3%
2755.051
0.3%
2752.051
0.3%
2751.81
0.3%
2744.21
0.3%
27421
0.3%
27321
0.3%
2716.31
0.3%

Last
Real number (ℝ≥0)

HIGH CORRELATION

Distinct323
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2405.912023
Minimum1913.05
Maximum2810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:06.580689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1913.05
5-th percentile1995.9
Q12245
median2424
Q32572
95-th percentile2703.5
Maximum2810
Range896.95
Interquartile range (IQR)327

Descriptive statistics

Standard deviation213.0669025
Coefficient of variation (CV)0.08855972307
Kurtosis-0.5486879634
Mean2405.912023
Median Absolute Deviation (MAD)151.3
Skewness-0.472091457
Sum820416
Variance45397.50496
MonotonicityNot monotonic
2022-09-11T09:55:06.815007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20903
 
0.9%
26683
 
0.9%
21122
 
0.6%
25302
 
0.6%
22252
 
0.6%
26352
 
0.6%
21782
 
0.6%
26262
 
0.6%
23822
 
0.6%
25892
 
0.6%
Other values (313)319
93.5%
ValueCountFrequency (%)
1913.051
0.3%
19181
0.3%
1921.51
0.3%
1926.81
0.3%
1927.11
0.3%
19321
0.3%
1933.91
0.3%
19371
0.3%
19561
0.3%
1958.851
0.3%
ValueCountFrequency (%)
28101
0.3%
2799.71
0.3%
2796.051
0.3%
2788.21
0.3%
27821
0.3%
2780.91
0.3%
27781
0.3%
27761
0.3%
2767.051
0.3%
2766.251
0.3%

Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct335
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2406.444282
Minimum1913.15
Maximum2819.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:07.064948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1913.15
5-th percentile1997.05
Q12250
median2424.4
Q32572.85
95-th percentile2700.4
Maximum2819.85
Range906.7
Interquartile range (IQR)322.85

Descriptive statistics

Standard deviation212.9288842
Coefficient of variation (CV)0.08848278177
Kurtosis-0.5467666656
Mean2406.444282
Median Absolute Deviation (MAD)154.25
Skewness-0.4746055498
Sum820597.5
Variance45338.70972
MonotonicityNot monotonic
2022-09-11T09:55:07.314889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2425.62
 
0.6%
20862
 
0.6%
2227.42
 
0.6%
2378.32
 
0.6%
2700.42
 
0.6%
2627.42
 
0.6%
2672.951
 
0.3%
2619.051
 
0.3%
2626.051
 
0.3%
2663.71
 
0.3%
Other values (325)325
95.3%
ValueCountFrequency (%)
1913.151
0.3%
1916.61
0.3%
1920.11
0.3%
1926.21
0.3%
19311
0.3%
1931.751
0.3%
1933.151
0.3%
1937.31
0.3%
1959.051
0.3%
1960.351
0.3%
ValueCountFrequency (%)
2819.851
0.3%
2798.751
0.3%
2790.251
0.3%
2782.11
0.3%
2780.451
0.3%
2779.51
0.3%
2778.351
0.3%
2775.651
0.3%
2772.751
0.3%
2767.551
0.3%

VWAP
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct341
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2407.293959
Minimum1915.34
Maximum2823.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:07.549210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1915.34
5-th percentile1999.87
Q12257.44
median2429.46
Q32574.11
95-th percentile2710.69
Maximum2823.91
Range908.57
Interquartile range (IQR)316.67

Descriptive statistics

Standard deviation212.6284907
Coefficient of variation (CV)0.0883267662
Kurtosis-0.5413777399
Mean2407.293959
Median Absolute Deviation (MAD)151.79
Skewness-0.4650870878
Sum820887.24
Variance45210.87506
MonotonicityNot monotonic
2022-09-11T09:55:07.783529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024.211
 
0.3%
2557.81
 
0.3%
2636.91
 
0.3%
2658.171
 
0.3%
2641.481
 
0.3%
26421
 
0.3%
2661.361
 
0.3%
26231
 
0.3%
2611.691
 
0.3%
2596.661
 
0.3%
Other values (331)331
97.1%
ValueCountFrequency (%)
1915.341
0.3%
1920.951
0.3%
1923.261
0.3%
1925.21
0.3%
1929.41
0.3%
1932.021
0.3%
1935.831
0.3%
1940.011
0.3%
1950.251
0.3%
1958.461
0.3%
ValueCountFrequency (%)
2823.911
0.3%
2817.631
0.3%
2794.981
0.3%
2793.341
0.3%
2783.291
0.3%
2775.791
0.3%
2773.481
0.3%
2773.031
0.3%
2768.551
0.3%
2763.871
0.3%

Volume
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct341
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7056064.891
Minimum787160
Maximum42209687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:08.017848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum787160
5-th percentile3455942
Q14768334
median6081627
Q37973235
95-th percentile12223037
Maximum42209687
Range41422527
Interquartile range (IQR)3204901

Descriptive statistics

Standard deviation4343831.713
Coefficient of variation (CV)0.6156167467
Kurtosis25.1994233
Mean7056064.891
Median Absolute Deviation (MAD)1505516
Skewness4.201837923
Sum2406118128
Variance1.886887395 × 1013
MonotonicityNot monotonic
2022-09-11T09:55:08.267790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80359151
 
0.3%
60771531
 
0.3%
60686631
 
0.3%
36593071
 
0.3%
36564081
 
0.3%
61027441
 
0.3%
72970281
 
0.3%
40076951
 
0.3%
45648911
 
0.3%
65636761
 
0.3%
Other values (331)331
97.1%
ValueCountFrequency (%)
7871601
0.3%
18539481
0.3%
22167081
0.3%
25020731
0.3%
28682351
0.3%
29243701
0.3%
29418831
0.3%
30440371
0.3%
30493041
0.3%
30837471
0.3%
ValueCountFrequency (%)
422096871
0.3%
378416711
0.3%
325914201
0.3%
272857821
0.3%
260608641
0.3%
255463341
0.3%
195684871
0.3%
193173351
0.3%
160980991
0.3%
155256441
0.3%

Turnover
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct341
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.693305829 × 1015
Minimum1.965800892 × 1014
Maximum9.211999493 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:08.502109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.965800892 × 1014
5-th percentile8.115922559 × 1014
Q11.142314756 × 1015
median1.458916963 × 1015
Q31.942001903 × 1015
95-th percentile3.004172516 × 1015
Maximum9.211999493 × 1015
Range9.015419404 × 1015
Interquartile range (IQR)7.996871468 × 1014

Descriptive statistics

Standard deviation1.021721954 × 1015
Coefficient of variation (CV)0.6033889074
Kurtosis23.4013569
Mean1.693305829 × 1015
Median Absolute Deviation (MAD)3.812314512 × 1014
Skewness3.97562863
Sum5.774172878 × 1017
Variance1.043915752 × 1030
MonotonicityNot monotonic
2022-09-11T09:55:08.736429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.626633943 × 10151
 
0.3%
1.554411585 × 10151
 
0.3%
1.600246615 × 10151
 
0.3%
9.72705226 × 10141
 
0.3%
9.658325027 × 10141
 
0.3%
1.612346563 × 10151
 
0.3%
1.942001903 × 10151
 
0.3%
1.051219846 × 10151
 
0.3%
1.192209306 × 10151
 
0.3%
1.704360617 × 10151
 
0.3%
Other values (331)331
97.1%
ValueCountFrequency (%)
1.965800892 × 10141
0.3%
4.38805404 × 10141
0.3%
5.722594257 × 10141
0.3%
5.98656851 × 10141
0.3%
6.500981051 × 10141
0.3%
6.524718783 × 10141
0.3%
6.808700909 × 10141
0.3%
7.02822628 × 10141
0.3%
7.179873286 × 10141
0.3%
7.285364915 × 10141
0.3%
ValueCountFrequency (%)
9.211999493 × 10151
0.3%
9.179980463 × 10151
0.3%
8.599389887 × 10151
0.3%
5.878142158 × 10151
0.3%
5.387879159 × 10151
0.3%
5.380107704 × 10151
0.3%
4.799499831 × 10151
0.3%
4.659492983 × 10151
0.3%
3.879858013 × 10151
0.3%
3.796726594 × 10151
0.3%

Trades
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct341
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240334.6979
Minimum63285
Maximum1165095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:08.986371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum63285
5-th percentile131393
Q1177038
median219641
Q3274696
95-th percentile408746
Maximum1165095
Range1101810
Interquartile range (IQR)97658

Descriptive statistics

Standard deviation104565.6109
Coefficient of variation (CV)0.4350832892
Kurtosis20.19525305
Mean240334.6979
Median Absolute Deviation (MAD)46248
Skewness3.229551421
Sum81954132
Variance1.093396698 × 1010
MonotonicityNot monotonic
2022-09-11T09:55:09.220691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2131531
 
0.3%
2213091
 
0.3%
2049331
 
0.3%
1675931
 
0.3%
1630601
 
0.3%
2494431
 
0.3%
2193161
 
0.3%
2038301
 
0.3%
1832611
 
0.3%
2769081
 
0.3%
Other values (331)331
97.1%
ValueCountFrequency (%)
632851
0.3%
895091
0.3%
1051901
0.3%
1056191
0.3%
1060301
0.3%
1074751
0.3%
1143451
0.3%
1165291
0.3%
1178551
0.3%
1184691
0.3%
ValueCountFrequency (%)
11650951
0.3%
7552781
0.3%
6423841
0.3%
6298471
0.3%
6241661
0.3%
5251221
0.3%
4975361
0.3%
4965451
0.3%
4934591
0.3%
4821751
0.3%

Deliverable Volume
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct341
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3494253.824
Minimum402932
Maximum19734107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:09.470631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum402932
5-th percentile1584484
Q12310132
median3012536
Q34117945
95-th percentile6735937
Maximum19734107
Range19331175
Interquartile range (IQR)1807813

Descriptive statistics

Standard deviation2018793.697
Coefficient of variation (CV)0.5777467234
Kurtosis19.85326346
Mean3494253.824
Median Absolute Deviation (MAD)880867
Skewness3.416603651
Sum1191540554
Variance4.075527993 × 1012
MonotonicityNot monotonic
2022-09-11T09:55:09.720574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28341031
 
0.3%
32089001
 
0.3%
39628901
 
0.3%
18598911
 
0.3%
18458311
 
0.3%
38934031
 
0.3%
42597341
 
0.3%
22809121
 
0.3%
21495651
 
0.3%
38212791
 
0.3%
Other values (331)331
97.1%
ValueCountFrequency (%)
4029321
0.3%
5323281
0.3%
10540221
0.3%
10828651
0.3%
10893961
0.3%
11606721
0.3%
12161611
0.3%
12300921
0.3%
13128101
0.3%
14364391
0.3%
ValueCountFrequency (%)
197341071
0.3%
161240971
0.3%
157421531
0.3%
98942471
0.3%
94607171
0.3%
92452551
0.3%
89252051
0.3%
86017791
0.3%
78570431
0.3%
77097661
0.3%

%Deliverble
Real number (ℝ≥0)

Distinct324
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5008601173
Minimum0.2611
Maximum0.7185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-11T09:55:09.970514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2611
5-th percentile0.3484
Q10.4531
median0.5119
Q30.5598
95-th percentile0.6337
Maximum0.7185
Range0.4574
Interquartile range (IQR)0.1067

Descriptive statistics

Standard deviation0.08567782192
Coefficient of variation (CV)0.1710613781
Kurtosis-0.007676732985
Mean0.5008601173
Median Absolute Deviation (MAD)0.0543
Skewness-0.4086686431
Sum170.7933
Variance0.007340689169
MonotonicityNot monotonic
2022-09-11T09:55:10.220455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45972
 
0.6%
0.59522
 
0.6%
0.47112
 
0.6%
0.54372
 
0.6%
0.49532
 
0.6%
0.57522
 
0.6%
0.48152
 
0.6%
0.55212
 
0.6%
0.52042
 
0.6%
0.58772
 
0.6%
Other values (314)321
94.1%
ValueCountFrequency (%)
0.26111
0.3%
0.26291
0.3%
0.28151
0.3%
0.28331
0.3%
0.28711
0.3%
0.28761
0.3%
0.2931
0.3%
0.29511
0.3%
0.30681
0.3%
0.30791
0.3%
ValueCountFrequency (%)
0.71851
0.3%
0.69021
0.3%
0.68581
0.3%
0.66891
0.3%
0.66671
0.3%
0.6591
0.3%
0.65351
0.3%
0.6531
0.3%
0.6491
0.3%
0.64531
0.3%

Interactions

2022-09-11T09:54:59.625048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:33.949271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:36.825410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:39.080475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:41.509251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:43.459515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:45.717011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:47.874861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:50.122594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:52.407872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:54.832392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:57.142913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:59.808087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:34.657916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:37.014618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:39.266793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:41.700062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:43.658819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.051438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:48.058872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:50.315480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:52.576000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:55.030361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:57.351393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:59.972562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:34.860907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:37.199345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:39.575622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:41.890009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:43.840207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.241066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:48.248013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:50.509588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:52.775431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:55.230513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:57.558044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:00.122184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:35.043442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:37.380366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:39.757431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:42.008039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:44.017341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.356451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:48.431103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:50.707441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:52.957937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:55.411163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:57.766337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:00.316701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:35.244777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:37.563866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:39.939012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:42.171510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:44.199760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.488019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:48.615436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:50.889453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:53.119481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:55.618694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:57.970359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:00.485501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:35.426722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:37.747613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:40.131789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:42.362583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:44.381367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.650682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:48.800504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:51.085764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:53.301180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:55.807590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:58.178629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:00.674663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:35.642154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:37.930421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:40.314403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:42.554389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:44.567356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.771551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:48.985143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:51.269738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:53.484590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:55.994358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:58.388062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:00.859160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:35.841094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:38.115360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:40.506025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:42.737590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:44.764028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:46.975132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:49.153046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:51.444233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:53.667342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:56.201816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:58.596147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:01.047215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:36.028758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:38.298987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:40.703264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:42.917311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:44.949234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:47.162958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:49.292054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:51.637740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:54.033168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:56.399588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:58.803082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:01.229843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:36.216427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:38.483001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:40.900662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:43.044279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:45.142544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:47.349320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:49.499048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:51.822487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:54.216830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:56.595194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:59.007698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:01.423696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:36.419302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:38.674675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:41.092780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:43.160250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:45.319425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:47.489322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:49.705868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:52.017743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:54.415193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:56.794978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:59.226330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:55:01.639585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:36.626622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:38.893933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:41.312120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:43.321398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:45.527611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:47.681966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:49.928573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:52.220052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:54.644399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:56.959047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-11T09:54:59.410535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-09-11T09:55:10.486020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-11T09:55:10.829688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-11T09:55:11.110871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-11T09:55:11.282711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-11T09:55:11.423300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-11T09:55:02.175747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-11T09:55:02.690758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%Deliverble
02021-04-29RELIANCEEQ1997.302022.902044.502007.302020.002024.052024.2180359151.626634e+1521315328341030.3527
12021-04-30RELIANCEEQ2024.052008.502036.001987.551995.901994.502010.2091509741.839532e+1528868739025040.4265
22021-05-03RELIANCEEQ1994.501966.001979.001943.101956.001959.051958.46109099422.136664e+1534380638005980.3484
32021-05-04RELIANCEEQ1959.051950.001967.801911.001918.001916.601935.83100836931.952033e+1532579844350710.4398
42021-05-05RELIANCEEQ1916.601923.351938.501908.051921.501920.101923.2657196491.100036e+1519099017624170.3081
52021-05-06RELIANCEEQ1920.101921.851935.001906.601932.001931.001920.9567492811.296502e+1522166628308350.4194
62021-05-07RELIANCEEQ1931.001937.751955.651926.001926.801931.751940.0156711631.100213e+1518867024184470.4264
72021-05-10RELIANCEEQ1931.751939.001946.801920.951927.101926.201932.0264338791.243036e+1519798629966970.4658
82021-05-11RELIANCEEQ1926.201915.001938.551910.001933.901933.151929.4062202171.200129e+1517597630355440.4880
92021-05-12RELIANCEEQ1933.151930.101932.901907.001913.051913.151915.3460816271.164838e+1521237827607970.4540

Last rows

DateSymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%Deliverble
3312022-08-26RELIANCEEQ2632.052633.002650.002607.002623.002618.002622.3049574491.299993e+1519131228856520.5821
3322022-08-29RELIANCEEQ2618.002585.002655.002578.052600.002597.652614.3491556842.393607e+1532600337370110.4082
3332022-08-30RELIANCEEQ2597.652614.002645.252576.602642.002637.952614.74106786702.792198e+1528565656243550.5267
3342022-09-01RELIANCEEQ2637.952582.652604.952550.702564.002560.402580.1391884342.370736e+1534641456589830.6159
3352022-09-02RELIANCEEQ2560.402560.402575.352525.702527.952530.502546.2459991961.527538e+1526615831853050.5310
3362022-09-05RELIANCEEQ2530.502531.002581.502531.002567.152569.802563.9252277851.340362e+1518961426584560.5085
3372022-09-06RELIANCEEQ2569.802573.002606.002572.002594.002596.852592.6850301721.304165e+1517442625886200.5146
3382022-09-07RELIANCEEQ2596.852575.002594.902570.002580.502581.752580.9534559428.919602e+1411785517241150.4989
3392022-09-08RELIANCEEQ2581.752588.252598.002571.002583.052585.402583.5432568408.414188e+1411652918211030.5592
3402022-09-09RELIANCEEQ2585.402610.002610.002564.002568.902569.302577.6938373029.891380e+1418594721494440.5601